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dc.contributor.authorSiti Zulaiha, Ahmad Ramdzan
dc.date.accessioned2016-06-16T06:30:35Z
dc.date.available2016-06-16T06:30:35Z
dc.date.issued2015-06
dc.identifier.urihttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/42077
dc.descriptionAccess is limited to UniMAP community.en_US
dc.description.abstractThis project presents the topic recognition of message threads in English using TF-IDF based on the distribution of similar words. First, a program is designed to extract the words from a sequence of sentences which taken from news articles. Next, a program is designed using TF-IDF with C programming language to compare words similarity between the words around the data and the words around the example sentences from Facebook, Twitter and blogs using TF-IDF coefficient. The similarity measures the occurrence of words around the word with all example sentences from collected. Finally, the performance of this proposed similarity measurement method is evaluated by measuring the precision, recall, and f-measure of the word identification. Furthermore, the test results presented the advantage and disadvantages of the proposed similarity measurement method that applied to classify the English word based on the distribution of similar words. Overall, the performance of our proposed method is good for word classification with three word extraction strategy.en_US
dc.language.isoenen_US
dc.publisherUniversiti Malaysia Perlis (UniMAP)en_US
dc.subjectMessage threadsen_US
dc.subjectN-Grams techniqueen_US
dc.subjectDynamics clustering techniqueen_US
dc.subjectEnglishen_US
dc.subjectEnglish wordsen_US
dc.titleTopic recognition of message threads in social networkingen_US
dc.typeLearning Objecten_US
dc.contributor.advisorDr. Nik Adilah Hanin Zahrien_US
dc.publisher.departmentSchool of Computer and Communication Engineeringen_US


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